Module 1: Participation 2
Group Paper Presentation
Overview
In this participation assignment, you will work in groups to study, synthesize, and present a foundational or state-of-the-art research paper in Generative AI, Large Language Models (LLMs), or reproducible computational practice.
The goal is not to reproduce the paper technically, but to:
- Understand why the paper mattered
- Explain what problem it solved
- Situate it within the modern GenAI stack
- Critically assess its assumptions, limitations, and downstream implications
Each group will deliver a short academic-style presentation aimed at a technically literate but non-specialist audience (e.g., analytics managers, graduate students, applied researchers).
This assignment emphasizes:
- Conceptual clarity
- Systems thinking
- Research literacy
- Responsible AI awareness
Goals
By completing this assignment, you will:
- Develop the ability to read and interpret AI research papers
- Learn how modern GenAI systems evolved from earlier computational ideas
- Practice explaining complex ideas clearly and precisely
- Engage critically with reproducibility, scale, alignment, and responsibility
- Strengthen academic and professional presentation skills
1 Paper Selection (One Paper per Group)
- Everyone must read through the curated list below.
- Papers are organized by theme.
- Each group will need to present a presentation on one of the papers from the week’s theme listed under numbered list.
- Names of the papers are listed in the table and are also available at the end of this document.
- You can also search these papers on scholar.google.com.
1.1 Open science & reproducibility (classic but still essential)
Reading instruction: Read these, no presentation.
- Wilson et al. (2014) Best Practices for Scientific Computing
- Peng (2011) Reproducible Research in Computational Science
- Stodden et al. (2014) The Practice of Reproducible Research
- Stodden et al. (2016) Computational Reproducibility
- Knuth (1984) *Literate Programming
1.2 Foundations Readings in Language Representation
Reading instruction: Read these, no presentation.
| Neural Foundations | Distributional Semantics | Embeddings in Practice |
|---|---|---|
| A Neural Probabilistic Language Model (Bengio et al. 2003) | Vector Space Models of Semantics (Turney and Pantel 2010) | Distributed Representations of Words (Mikolov, Sutskever, et al. 2013) |
| Sequence to sequence learning (Sutskever et al. 2014) | Are LLMs Models of Distributional Semantics? A Case Study on Quantifiers (Enyan et al. 2024) | Efficient Estimation of Word Representations in Vector Space (Mikolov, Chen, et al. 2013) |
1.3 Group 1 – Transformers: Architecture and Attention
| Transformer Core | Interpretability & Attention | Representational Power |
|---|---|---|
| Attention Is All You Need (Vaswani et al. 2017) | What Does BERT Look At? (Clark et al. 2019) | On the Turing Completeness of Modern Neural Network Architectures (Pérez et al. 2019) |
| Sequence to Sequence Learning (Sutskever et al. 2014) | Quantifying attention flow in transformers (Abnar and Zuidema 2020) | Are Transformers Universal Approximators? (Yun et al. 2019) |
| Self-Attention with Relative Position (Shaw et al. 2018) | Attention Is Not Explanation (Jain and Wallace 2019) | Efficient streaming language models(Xiao et al. 2023) |
1.4 Group 2 – Scaling & Emergence
- Scaling Laws for Neural LMs (Kaplan et al. 2020)
- Sparks of AGI (Bubeck et al. 2023)
2 Presentation Expectations
2.1 Presentation Length
- 10–12 minutes total
- 5–7 slides
- All group members must participate
2.2 Suggested Slide Structure
Your presentation must include:
Problem Framing
- What problem did this paper address?
- Why was it important at the time?
Core Contribution
- Key idea, model, framework, or insight
- What changed because of this paper?
Technical Intuition (Not Math-Heavy)
- Diagrams encouraged
- Focus on system logic, not equations
Impact and Legacy
- How does this paper influence modern GenAI systems?
- Where do we see it today?
Limitations and Critique
- What does the paper not address?
- What assumptions may no longer hold?
Relevance to This Course
How does this connect to:
- Prompting
- RAG
- Fine-tuning
- Reproducibility
- Responsible AI
3 Submission Requirements
- Presentation Slides (PDF)
- One-page Paper Brief (PDF) including:
- Paper citation
- Key contribution (≤150 words)
- One critique
- One open research question
3.1 Hints and Best Practices
- Focus on ideas, not implementation details
- Assume your audience understands Python and ML basics
- Use diagrams over equations
- Avoid reading slides verbatim
- Practice explaining the paper without jargon
This assignment is designed to help you think like a researcher and systems designer, not just a tool user. Choose wisely, read deeply, and present with clarity.